The present invention relates to a method for determining the driving situation of a motor vehicle and a corresponding system.
Due to the growing volume of information made available in a motor vehicle and the associated demands on the driver, some well-directed relief is required when the burden is high due to the traffic situation. The present invention provides a method and a system for relieving the driver.
One aspect of the inventive method is the use of data provided in the motor vehicle, representing the value of at least one state variable of the vehicle. This data may be made available via the data bus of the vehicle, for example, for implementation of the inventive method. In a first step, a data record providing the history of the at least one state variable is supplied. In a second step, a neural network is made available by a suitably programmed computer in the motor vehicle. The neural network of the inventive method can have at least one input layer and one output layer, each layer having a plurality of perceptrons. In a third step, the respective value of the at least one state variable of the respective point in time, can be standardized to the range of 0 to 1, like all the other values, is sent to one perceptron of the neural network, the current driving situation then being output by the perceptrons of the output layer of the neural network after being trained.
A perceptron is a mathematical function (software function) formed by software, calculating from input values an output value that is relayed to various perceptrons. The input values are weighted and the output value of the perceptron is a mapping function of the weighted input values according to the software function.
Exemplary embodiments of the present invention are explained in greater detail below.
An exemplary neural network is a sigmoid network that can have three layers. Each perceptron in this case is formed by the sigmoid function, which is essentially known. Sigmoid networks are advantageously characterized in that the output value of the perceptron, i.e., the software function is largely linear to the output value, which simplifies further processing.
The prevailing driving state of the vehicle can be ascertained, i.e., determined, from a chronological sequence of driving situations that have been detected. A prevailing driving state is assigned to a chronological sequence of driving situations that have been detected on the basis of at least one assignment specification. Instead of the early driving state, a new driving state can be determined only when the new driving state has already been ascertained, i.e., determined, repeatedly within an interval of time that has elapsed.
A different situation can be assigned to each perceptron of the output layer and/or its output signal. The maximum output signal of all the output signals of the perceptrons of the output layer indicates the current driving situation of the motor vehicle.
The output signal, preferably the signal peak of the first perceptron of the output layer of the neural network can indicate a “stop and go” driving situation. The output signal, such as a signal peak, of the second perceptron of the output layer of the neural network can be defined by the “city traffic” driving situation. The output signal, can be a signal peak of the third perceptron of the output layer of the neural network stands for the “cruising” driving situation. The output signal, can be the signal peak, of the fourth perceptron of the output layer of the neural network stands for the “sporty” driving situation. In summary, the input layer of the neural network, i.e., the corresponding computer program in this embodiment of the present invention, is chronologically supplied with a data record having state variables of the motor vehicle and the driving situation is determined chronologically on the basis of the maximum output signal of all perceptrons of the output layer. It is self-evident that the driving situation thereby determined may also be grouped into a larger or smaller number of classes (“stop and go,” etc.).
In an exemplary embodiment of the present invention, the driving situation is considered to be nonspecific when the difference between the value of the maximum output signal of all perceptrons of the output layer and the value of the next smaller output signal of the respective perceptron is less than 20%. The same thing is also true alternatively or additionally in another exemplary embodiment when the value of the greatest output signal of all perceptrons of the output layer is smaller than 10% of its maximum value. For these optional inventive measures it is possible to take into account only driving situations that have been determined with sufficient certainty. This is true in particular of determination of driving state based on the driving situation ascertained.
In another exemplary embodiment of the present invention, the extent of the information to be relayed to the driver of the vehicle is based on the driving state ascertained. With the inventive method and/or system, information with a high deflection such as a telephone call can be withheld from the driver temporarily during a driving state which makes high demands on the driver, e.g., rapid driving on the autobahn, i.e., highway, and/or it is saved for later display, e.g., in a less demanding driving situation.
In an inventive embodiment and/or an inventive man-machine interface, the driver is able to select which information and/or which totality of information in a driving situation of the first class such as “stop and go” is to be displayed and/or output for him in a second class, e.g., “city driving,” etc.
Other objects, advantages and novel features of the present invention will become apparent from the following detailed description of the invention when considered in conjunction with the accompanying drawings.